Autoregressive Research Articles

Sudden stop: Supply and demand shocks in the German natural gas market

SummaryWe use a structural vector autoregressive (SVAR) model to study the German natural gas market and investigate the impact of the 2022 Russian supply stop on the German economy. Combining conventional and narrative sign restrictions, we find that gas supply and demand shocks have large and persistent price effects, while output effects tend to be moderate. The 2022 natural gas price spike was driven by adverse supply shocks and positive storage demand shocks, as Germany filled its inventories before the winter. Counterfactual simulations of an embargo on natural gas imports from Russia indicate similar positive price and negative output effects compared with what we observe in the data.

MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION

The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime

Time-Varying Role of House Prices on Monetary Policy in Türkiye

The main focus of this study is examining the time-varying effects of house prices on monetary policy in Türkiye from February 2010 to November 2021. We analyse these effects using a time-varying parameter vector autoregressive model with stochastic volatility (TVP VAR-SV). The evidence indicates that responses of inflation to positive house price shocks decrease over time. The response of credit and interest rates to positive house price shocks increases after 2018. The results reveal that the responses of the Central Bank of the Republic of Türkiye (CBRT) to the changes in the housing market have changed over time.

Analysis of Economic Growth in East Nusa Tenggara Province Using Spatial Regression Model

This research aims to examine the significant factors influencing economic growth in the East Nusa Tenggara Province. The estimation is carried out through a spatial regression model approach. The research variables were selected based on the economic growth model proposed by Mankiw-Romer-Weil, namely Gross Regional Domestic Product at constant prices (Y), Labor ( X1), Electricity Consumption (X2 ), Regional Original Income (X3 ), Capital Expenditure ( X4), and Schooling Duration (X5 ). The research data consists of secondary data from the year 2022 in 22 regencies/cities obtained from the Central Statistics Agency (BPS) of East Nusa Tenggara Province. Modeling with the Ordinary Least Squares (OLS) regression method resulted in three significant independent variables at α=5%: local revenue, capital expenditure, and average years of schooling. Based on diagnostic tests, there was spatial dependence in lag and error, leading to the use of Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) for spatial regression. From these spatial models, the significant independent variables influencing economic growth in the 22 regencies/cities in East Nusa Tenggara were local revenue, capital expenditure, and average years of schooling. The queen contiguity weighting was used. Based on R2 and AIC criteria, the best spatial regression model was the Spatial Error Model (SEM) with the highest R2 of 0.837610 and the smallest AIC value of 393.03. For further research, it is recommended to consider local factors that may influence economic growth, such as the sustainability of the agricultural or tourism sector, which may vary in each region.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

Impact of migrants on communicable diseases in Thailand

BackgroundWhile foreign migrants contribute to economic development, they may impact public health by transmitting communicable diseases to the local population. With its geopolitical position, Thailand has been a primary destination for migrants from neighbouring countries in Southeast Asia and beyond. This positioning makes it a focal point for examining the complexities of migration dynamics and its implications for public health. Through a quantitative analysis, this paper investigates the influence of foreign migrants on physical health issues in Thailand, exploring their impact on various types of communicable diseases. The utilization of provincial-level data from Thailand offers insights into the localized effects of migrant populations on public health within the country. These insights can serve as a valuable resource for researchers and policymakers who conduct comparative analyses, facilitating a deeper understanding of the complex relationship between international migration and public health worldwide.MethodsA spatial panel autoregressive model (SAR) is applied on the provincial level communicable diseases and socio-economic data in Thailand from the period 2016 to 2021.ResultsThe results indicate that the influence of foreign migrants on communicable diseases in Thailand varies depending on the type of disease. While an increase in migrants correlates with a higher prevalence of respiratory and other communicable diseases, it conversely reduces the prevalence of vaccine-preventable diseases. Additionally, we found that migrants do not significantly impact the prevalence of food- and water-borne diseases, insect-borne diseases, animal-borne diseases, or sexually transmitted diseases in Thailand. Additionally, other factors, such as GPP per capita, unemployment, poverty, and technology access, strongly correlate with most types of communicable diseases.ConclusionAs revealed by this study, the increase in migrants leads to a rise in respiratory and other communicable diseases, as well as a decrease in vaccine-preventable diseases, which carries significant policy implications. These results urge policymakers, the Ministry of Labour, and the Ministry of Public Health to implement tailored policies and measures to enhance public health and effectively mitigate the risk of communicable diseases transmitted by migrants in the future.

Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks.

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the "regression to average" issue of autoregressive model leads to the "blurring" phenomenon. The evolution method's generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method's generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the "regression to average" issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.

BELHD: improving biomedical entity linking with homonym disambiguation.

Biomedical entity linking (BEL) is the task of grounding entity mentions to a given knowledge base (KB). Recently, neural name-based methods, system identifying the most appropriate name in the KB for a given mention using neural network (either via dense retrieval or autoregressive modeling), achieved remarkable results for the task, without requiring manual tuning or definition of domain/entity-specific rules. However, as name-based methods directly return KB names, they cannot cope with homonyms, i.e. different KB entities sharing the exact same name. This significantly affects their performance for KBs where homonyms account for a large amount of entity mentions (e.g. UMLS and NCBI Gene). We present BELHD (Biomedical Entity Linking with Homonym Disambiguation), a new name-based method that copes with this challenge. BELHD builds upon the BioSyn model with two crucial extensions. First, it performs pre-processing of the KB, during which it expands homonyms with a specifically constructed disambiguating string, thus enforcing unique linking decisions. Second, it introduces candidate sharing, a novel strategy that strengthens the overall training signal by including similar mentions from the same document as positive or negative examples, according to their corresponding KB identifier. Experiments with 10 corpora and 5 entity types show that BELHD improves upon current neural state-of-the-art approaches, achieving the best results in 6 out of 10 corpora with an average improvement of 4.55pp recall@1. Furthermore, the KB preprocessing is orthogonal to the prediction model and thus can also improve other neural methods, which we exemplify for GenBioEL, a generative name-based BEL approach. The code to reproduce our experiments can be found at: https://github.com/sg-wbi/belhd.

Agricultural economic security under the model of integrated agricultural industry development

As globalization and technological advancements progress, the integrated agricultural industry development model has become crucial for agricultural modernization. This model fosters inter-industry integration, enhancing agricultural competitiveness and sustainability. However, it also introduces new challenges, such as increased risk spillover and outdated regulatory mechanisms. This study employs a vector autoregression (VAR) model and a directed weighted risk spillover network for a quantitative analysis of risk dynamics within agricultural markets, identifying pathways and influencers of risk transmission across industry segments. A blockchain-based regulatory framework is proposed to improve traceability, regulatory efficiency, and data security in agricultural product management. This includes the development of registration processes, smart contracts, consensus mechanisms, and data upload protocols. The research expands the theoretical base of agricultural economic security and offers practical policy guidance, with substantial academic and practical implications. It highlights how blockchain can address market trust issues, mitigate information asymmetry, and reduce potential risks, contributing to the healthier development of agricultural economy.

Modeling Exchange Rate of Naira to Euro with the APLSTAR-GARCH model

Application of the asymmetric power logistic smooth transition autoregressive (APLSTAR) model proposed by [1] to naira/Euro exchange rate spanning from January, 2006 to April, 2021, which is a nonlinear macroeconomic time series was considered. The APLSTAR model was justifiably fitted to the series and the fit of the APLSTAR model compared with the fits of the competing models revealed that the APLSTAR model fits the data exchange rate of naira to Euro better than the other asymmetric STAR models. Lagrange Multiplier tests for autoregressive conditional heteroscedastic (ARCH) effects were carried out and there was no substantial evidence to reject the presence of ARCH effects in the set of residuals used. Hence, we compared hybrid smooth transition autoregressive-generalized ARCH (STAR-GARCH) models using model evaluation criteria. On balance, the APLSTAR-GARCH (0, 1) model outperforms the other models under consideration.

Inflation effects of oil and gas prices in the UK: Symmetries and asymmetries

The United Kingdom is among the countries experiencing a cost-of-living crisis believed to be influenced, at least in part, by the dynamics of the international oil and gas markets. To this end, this study aims to achieve to achieve two objectives. Firstly, the dynamic association between the UK's inflation and oil and gas prices is examined. Further, the study examines whether the response of the UK's inflation to energy price dynamics is (a)symmetric. This study adopts wavelet coherency to determine the dynamic co-movement between energy prices and inflation. In addition, the dynamic simulated autoregressive distributed lag model (DS-ARDL) is used to examine the dynamic response of inflation to energy price changes. The estimation results reveal a symmetric response of inflation to gas price shocks. Further, the response of inflation to oil price shocks is asymmetric. Interestingly, the effect of gas price dynamics passes more strongly to inflation than to oil price dynamics. These findings suggest that a more diversified energy mix could help prevent substantial energy price pass-through to inflationary pressures.

The rise of Soybean in international commodity markets: A quantile investigation

The complex interplay between agricultural and energy commodities has been a subject of interest in past research, gaining more relevance recently due to geopolitical events such as the conflict between Ukraine and Russia. This conflict has systematically driven up the prices of both energy and agricultural commodities. Deeply understanding the dynamic interconnections between these commodities and the cascading events resulting from the war is crucial for comprehensive market analysis. Our study leverages the connectedness or risk of spillover based on a Quantile Vector Autoregression (QVAR) model, allowing us to track connectedness over time through the examination of extreme quantiles. This approach facilitates the identification of shocks triggered by exogenous events, such as the Russian-Ukrainian war, which are often observable in these extreme quantiles or tails. The investigation encompasses several agricultural commodities: Wheat, Barley, Soybean, Soybean Oil, Soybean Meal, and Sunflower Oil, along with energy commodities represented by Crude Oil and Natural Gas. Furthermore, we considered the prices of crucial fertilizers, DAP & Urea, given their significance in agricultural production. The timeframe for our study extends from January 2010 to January 2023, providing a comprehensive review of market trends during various geopolitical scenarios. This research contributes valuable insights into the intersection of global events, agricultural trends, and energy commodity markets. The study revealed that Soybean and its derivatives consistently play a leading role in the market, with Soybean being the primary shock transmitter. This is particularly true for the upper Quantile, where Soybean and Soybean Meal's influence remains stable. On the other hand, Soybean Oil's, Barley, and Wheat risk of spillover has increased, especially during the Ukraine-Russia conflict. Finally, spillover appears symmetric, with both extreme tails exhibiting around 91–87 % connectedness, while the median Quantile is under 49 %. We observed a diminution in network complexity, manifested as a decline in network connectedness, in correlation with extreme quantiles. Policymakers can use this information to draft proactive measures, ensuring stability and sustainability in both domestic and international markets.

Nonlinear network connectedness: Assessing financial risk transmission in MENA and influence of external financial conditions

This study investigates the influence of global financial market conditions on financial risk connectedness and transmission among the Middle East and North Africa (MENA) economies. Utilizing weekly realized stock market volatilities as a measure of risk and employing a smooth transition threshold vector autoregressive (STVAR) model to analyze risk transmission under varying levels of financial stress, the authors also examine the impact of external macroeconomic conditions on the risk connectedness of MENA economies. The results indicate that the overall connectedness, based on a standard VAR model, is moderate at 48.34%. However, in the low financial stress regime, overall connectedness increases to 52.79%, and in the high financial stress regime, it rises to 72.94%, indicating stronger risk interdependency among MENA countries during times of high stress. In the high financial stress regime, Kuwait, Oman, Qatar, Saudi Arabia, Turkey, and the United Arab Emirates are identified as net risk transmitters among MENA countries. The study also reveals that risk transmission across MENA is more pronounced in the regime-dependent model compared to the overall mean-based VAR model.

Jobless recoveries and time variation in labor markets

We study the nature of changes in the relationship between the labor market and output by using a broader set of labor market variables and a flexible time-varying-parameter vector autoregression model with stochastic volatility (TVP-SV). Overall, the TVP-SV model fits the labor market data well and has good predictive performance. We find heterogeneity both in the timing and the type of changes in the relationship between labor market variables and output. We find evidence that the relationship between output and employment growth, hours, vacancies, and wages has changed over time, with employment, wages, and vacancies being less responsive over time. In contrast, the responses of hours and part-time employment to output have become stronger over time, indicating a shift towards utilization of the intensive margin and towards utilizing more flexible labor inputs.

Fixed accuracy confidence intervals for variance under first-order stationary autoregressive processes

Fixed accuracy confidence intervals for variance under first-order stationary autoregressive processes

Climate risk and energy futures high frequency volatility prediction

This study explores the impact of climate risk on the volatility of energy futures, including heating oil, crude oil, and natural gas. In particularly, employing the heterogeneous autoregressive model and its extensions, we find that incorporating climate risk information significantly improves predictive accuracy for high-frequency volatility of energy futures both in- and out-of-sample. These results are robust across different forecasting mechanisms and periods. Additionally, we demonstrate that climate risk provides incremental information compared with existing predictors of energy futures volatility, leading to improved prediction accuracy when combining various predictors. Finally, we demonstrate that considering climate risk can lead to increased economic gains for investors in heating oil and crude oil markets. Our findings provide valuable insights for market participants in the realms of energy portfolio selection and risk management.

Autoregressive double latent variables probabilistic model for higher-order dynamic process monitoring

The application of multivariate statistical analysis in process monitoring has emerged as a significant research topic, with a focus on consideration of data correlations. The present study investigates an anomaly detection method based on autoregressive double latent variables probabilistic (ADLVP) model for industrial dynamic processes. Specifically, the ADLVP model incorporates two distinct types of latent variables (LVs) to capture the internal relationships within the data from both quality-correlated and uncorrelated perspectives. Moreover, the model employs autoregressive modeling to characterize the double latent variables with time-dependence, enabling them to unveil more intricate higher-order autocorrelations between industrial measurements. The model parameters and the double latent variables can be iteratively determined using the expectation maximization (EM) algorithm, upon which the statistics for process monitoring are devised. Finally, the proposed method is validated in two industrial studies, and experimental results demonstrate that the ADLVP model outperforms its counterparts in dynamic processes monitoring.

Linear and nonlinear time-series methodologies for bridge condition assessment: A literature review

Railway bridges are essential components of any transportation system and are typically subjected to several environmental and operational actions that can cause damage. Furthermore, they are not easily replaced, and their failure can have catastrophic consequences. Considering the expected lifespan of bridges, it is essential to guarantee their adequate serviceability and safety. In this scenario, emerges the Structural Health Monitoring (SHM), which allows the early identification of damage before it becomes critical. Damage identification is usually performed by the comparison between the damaged and undamaged responses obtained from monitoring data. Among the several features extracted from the responses, the time-series models exhibit a better performance, capability of early damage detection, and may also be applied within online damage detection strategies using unsupervised machine learning frameworks. In this paper, a review of advanced time-series methodologies for damage detection is presented. Initially, several time-series models often used in SHM are described, such as Autoregressive Models (AR), Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). Later, the framework where these models are usually applied is also detailed, including the latest upgrades and most relevant results. Finally, the conclusions summarize and elucidate the current perspectives and research gaps on the time-series models.

Do Technological Innovation and Environmental Regulation Reduce Carbon Dioxide Emissions? Evidence from China

<p style="text-indent:0cm; text-align:justify"><span style="font-size:12pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif">The Chinese industry holds a significant position in the national economy. However, industrial carbon dioxide (CO<sub>2</sub>) emissions account for a large proportion of the total CO<sub>2</sub> emissions, which has a negative impact on the environment. To identify the factors affecting industrial CO<sub>2</sub> emissions, a vector autoregressive (VAR) model system is established to empirically test the factors influencing industrial CO<sub>2</sub> emissions, using data on industrial technology innovation (TI), environmental regulation, and CO<sub>2</sub> emissions from 2000 to 2023 in China. The results show that there is a cointegration relationship between industrial technological innovation, environmental regulation and CO<sub>2</sub> emissions. Each unit increase in environmental regulation will reduce 2.132 units of CO<sub>2</sub> emissions. Meanwhile, each unit increase in technological innovation results in a decrease of 0.067 units of CO<sub>2</sub> emissions. Compared with TI, environmental regulation has a greater impact on CO<sub>2</sub> emission reduction. The effects of the impulses of the stochastic perturbation terms of industrial TI, environmental regulation, and CO<sub>2</sub> emissions on the current and future values of industrial TI, environmental regulation, and CO<sub>2</sub> emissions in the VAR system are de-picted through the VAR impulse response function. The contribution of each new interest shock to the change of industrial TI, environmental regulation and CO<sub>2</sub> emissions is analyzed by variance decomposition. This paper enriches the application of institutional theory and technological in-novation theory in CO<sub>2</sub> emission reduction and also provides a reference for relevant departments to formulate emission reduction policies and industrial technological innovation.</span></span></span></p>

Association between Dietary Practices and Growth Trajectories in Preschool Children.

To estimate the association of dietary practices with early life weight among children born in 2001 in the U.S. The Early Childhood Longitudinal Study (ECLS-B) is a population-based cohort followed from 9 months to Kindergarten entry (n~5000). We employed an auto-regressive cross-lagged model to assess the impacts of dietary practices on weight stability over time. We calculated age- and sex-standardized BMI z-scores using WHO growth curves. BMI z-scores had a stable increase over time. Later age of solid food introduction was associated with lower BMI z-score at age 2y [-0.03(-0.05,-0.004)]; having dinners more times per week with family at age 2y was associated with higher BMI z-score at 4y [0.03(0.01,0.05)]. These changes, of <0.05 BMI z-score, are small. Children with higher birthweight were introduced to solid foods at younger ages [-0.39 months(-0.45,-0.32)] and were breast fed longer [0.13 months(0.04,0.22)]. For girls, having dinner at a regular time at ages 2y and 4y was associated with lower BMI at ages 4y and 5y. Dietary practices in early childhood affect weight trajectories. However, these effects are small and are largely overshadowed by prior weight. Children's weight in infancy is associated with subsequent dietary practices but also small effects.